---

output: flexdashboard::flex_dashboard: orientation: columns vertical_layout: fill —

Since the original csv data is too large and I cannot commit it to github I filtered the data beforehands and saved it as a new csv file called “nyc_inspect_hw” before I commited the creteria is manhattan and score of greater than 10 and less than 50

The following is my code:

nyc_inspec = read_csv(file=“./nyc_inspec.csv”)%>% select(camis,boro,cuisine_description,score,latitude,longitude)%>% filter(boro==“Manhattan”)%>% filter(score>10)%>% filter(score<50)

write.csv(nyc_inspec,“./nyc_inspec_hw.csv”)

Load the Data, filter the data with restaurants in Manhattan only.

---

Chart A

Create a scatter plot, of the geographical distribution of the restaurants in Manhattan,adjusting for x-axis as lattitude and y-axis longitude to fit the graph

nyc_inspec %>%
  plot_ly(
    x = ~latitude, y = ~longitude, type = "scatter", mode = "markers",
    color = ~score, text= ~cuisine_description, alpha = 100)%>%
  layout(
    xaxis = list(
      range=c(40.7,40.85)
        ),
    yaxis = list(
      range=c(-74.02,-73.92)
    )
  )

---

Chart B

Create a box plot counting scores of each types of cuisines according to their score distribution.

nyc_inspec %>% 
  mutate(cuisine_description = fct_reorder(cuisine_description, score)) %>% 
  plot_ly(y = ~score, color = ~cuisine_description, type = "box", colors = "viridis")%>%
  layout(
  
    yaxis = list(
      range=c(0,60)
    )
  )

---

Chart C

Create a bar graph counting types of restaurants in Manhattan.

nyc_inspec %>% 
  count(cuisine_description) %>% 
  mutate(Type = fct_reorder(cuisine_description, n)) %>% 
  plot_ly(x = ~Type, y = ~n, color = ~Type, type = "bar", colors = "viridis")